Run-time monitoring with adjustable overhead using dataflow-guided filtering

Recent studies have proposed various parallel runtime monitoring techniques to improve the reliability, security, and debugging capabilities of computer systems. However, these run-time monitors can introduce large performance and energy overheads, especially for flexible systems that support a range of monitors. In this paper, we introduce a hardware dataflow tracking engine that enables adjustable overhead through partial monitoring. This allows a trade-off to be made between monitoring coverage and overhead. This dataflow engine can also be extended to filter out monitoring operations associated with null metadata in order to reduce overhead. Given this architecture, we investigate how the dropping decisions should be made for partial monitoring and show that there exist interesting policy decisions depending on the target application of partial monitoring. Our experimental results show that overhead can be reduced significantly by trading off coverage. For example, for monitoring techniques with average overheads of 2-6x, the proposed architecture is able to reduce overhead to 1.5x while still achieving 14-85% average coverage.